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市場調查報告書

商品編碼

2064874

邊緣人工智慧推理晶片市場預測至2034年—按晶片類型、製程節點、功耗、應用、最終用戶和地區分類的全球分析

Edge AI Inference Chips Market Forecasts to 2034 - Global Analysis By Chip Type, Process Node, Power Consumption, Application, End User and By Geography

出版日期: 2026年06月11日 | 出版商:

Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

簡介目錄圖表

根據 Stratistics MRC 的數據，預計到 2026 年，全球邊緣 AI 推理晶片市場規模將達到 246 億美元，並在預測期內以 7.4% 的複合年成長率成長，到 2034 年將達到 436 億美元。

邊緣AI推理晶片是指專用的半導體處理器，旨在無需依賴雲端連接，即可在邊緣設備本地運行人工智慧（AI）和機器學習推理工作負載。這些晶片整合了專用神經處理單元、硬體加速器和最佳化的記憶體架構，可在有限的功耗預算內提供高吞吐量、低延遲的AI運算。邊緣AI推理晶片提供CPU、GPU、ASIC、FPGA和系統晶片（SoC）等多種配置，可支援智慧型手機、監控系統、自動駕駛汽車、無人機和工業機器人等設備的即時電腦視覺、自然語言處理、感測器融合和自主導航等功能。

人工智慧加速自動駕駛汽車

隨著自動駕駛汽車和高級駕駛輔助系統（ADAS）在全球範圍內的快速商業化，對能夠即時處理多感測器資料流並確保關鍵安全可靠性的高性能邊緣AI推理晶片的需求顯著成長。車規級推理處理器必須在嚴格的功耗和散熱限制下同時執行電腦視覺、雷射雷達融合和路徑規劃演算法。領先的汽車製造商和一級供應商正在將專用AI推理晶片整合到其下一代ADAS平台中。法律規範對車輛自動化提出了更高的安全標準，這進一步加速了專用車規邊緣AI推理晶片的普及應用。

複雜的晶片設計和檢驗週期

開發針對特定應用工作負載最佳化的邊緣人工智慧推理晶片需要大規模的客製化晶片設計、檢驗和認證流程，這涉及數年的開發週期和數億美元的工程投資。針對各種邊緣部署環境協同最佳化運算架構、記憶體層和電源管理電路的複雜性，是新參與企業面臨的主要障礙。此外，人工智慧模型架構的快速演進需要持續的晶片重新設計，這使得供應商面臨著如何在不犧牲產品上市速度的前提下，保持產品在不同世代間的競爭優勢的挑戰。

物聯網邊緣智慧的普及

物聯網設備的爆炸性成長，使得無需持續連接雲端即可實現本地人工智慧處理能力的需求日益成長，這為邊緣人工智慧推理晶片供應商創造了巨大的商機。智慧家庭設備、工業感測器、農業監測系統和零售分析平台等都在推動對嵌入式人工智慧推理以進行即時決策的需求。從依賴雲端的人工智慧架構轉向邊緣原生人工智慧架構的轉變，也促使市場對能夠使用電池或能源採集供電的超低功耗推理晶片的需求不斷成長。能夠提供涵蓋從微控制器到高效能運算等各種功耗和性能的可擴展晶片系列的供應商，將最有優勢抓住這一機會。

地緣政治因素導致半導體供應中斷

地緣政治緊張局勢加劇以及針對先進半導體技術的出口限制，正給依賴集中於特定地理區域的專業製造流程和設備的邊緣人工智慧推理晶片製造商帶來重大的供應鏈風險。對先進晶片製造設備和7奈米以下製造服務的出口限制，限制了依賴尖端製程技術的供應商的產品藍圖的執行。客戶對供應商供應鏈多元化的要求日益提高，這增加了製造的複雜性和成本。這些地緣政治發展為採購帶來了不確定性，並可能導致企業和政府採用邊緣人工智慧推理解決方案的計畫出現延誤。

新型冠狀病毒（COVID-19）的影響：

新冠疫情擾亂了全球半導體供應鏈，導致邊緣人工智慧晶片嚴重短缺，並延緩了汽車、工業和家用電子電器領域的部署計畫。然而，疫情同時也加速了數位轉型和遠端監控的需求，提升了對邊緣人工智慧推理能力的長期需求。疫情後供應鏈的韌性以及對國內半導體製造能力的投資，增強了邊緣人工智慧晶片市場在預測期內持續成長的結構基礎。

在預測期內，基於 SoC 的推理加速器細分市場預計將成為最大的細分市場。

預計在預測期內，基於系統單晶片 (SoC) 的推理加速器細分市場將佔據最大的市場佔有率。這是因為其高度整合的設計，將 CPU、GPU、神經網路處理單元 (NPU)、記憶體控制器和周邊設備介面整合在單一晶片封裝內，從而在主流邊緣 AI 應用中實現了最佳的每瓦性能效率。在家用電子電器、智慧相機和物聯網閘道應用中，基於 SoC 的解決方案因其成本效益和緊湊的尺寸而備受青睞。多核心 SoC 架構和 AI 最佳化指令集的持續進步，鞏固了該細分市場在各種邊緣部署環境中的商業性主導地位。

預計在預測期內，28 nm 以上的段將呈現最高的複合年成長率。

在預測期內，28nm及以上製程的晶片預計將呈現最高的成長率，這主要得益於對價格敏感的物聯網、工業感測器和嵌入式運算應用領域對具成本效益邊緣AI推理晶片的強勁需求，這些應用並不需要最先進的製程節點。成熟的28nm以上製程節點具有更低的單位成本、更高的生產可用性，並且在大批量邊緣部署中擁有久經考驗的長期供應可靠性。此外，邊緣AI在農業、基礎設施監控和零售應用領域的日益普及（在這些領域，最大限度地降低晶片成本至關重要）也進一步推動了該細分市場的快速擴張。

市佔率最大的地區：

在整個預測期內，北美預計將保持最大的市場佔有率。這主要得益於該地區擁有眾多領先的人工智慧晶片設計公司，例如英偉達（NVIDIA）、英特爾（Intel）、高通（Qualcomm）和蘋果（Apple），以及自動駕駛汽車、國防和工業人工智慧應用領域研發專案的集中度最高。北美無晶圓半導體生態系統的強大基礎，以及創投公司和大型企業對人工智慧晶片創新的大量研發投入，正在鞏固該地區的技術領先地位。美國政府支持國內半導體製造和人工智慧基礎設施投資的舉措，也進一步鞏固了北美的市場地位。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於其龐大的消費性電子產品產量、5G和物聯網設備的快速普及，以及中國、韓國、台灣和日本積極推進的國內半導體研發計畫。該地區龐大的智慧型手機和智慧型裝置生產基地持續推動對邊緣人工智慧推理晶片的高需求。政府的半導體自給自足戰略以及對國內晶片設計和製造能力的大量公共投資，預計將在整個預測期內加速該地區產能的擴張。

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所有購買此報告的客戶均可享受以下免費自訂選項之一：

企業概況
- 對其他市場參與者（最多 3 家公司）進行全面分析
- 對主要公司進行SWOT分析（最多3家公司）
區域細分
- 應客戶要求，我們提供主要國家的市場估算和預測，以及複合年成長率（註：需進行可行性檢查）。
競爭性標竿分析
- 根據產品系列、地理覆蓋範圍和策略聯盟對領先公司進行基準分析。

北美洲
- 美國
- 加拿大
- 墨西哥
歐洲
- 英國
- 德國
- 法國
- 義大利
- 西班牙
- 荷蘭
- 比利時
- 瑞典
- 瑞士
- 波蘭
- 其他歐洲國家
亞太地區
- 中國
- 日本
- 印度
- 韓國
- 澳洲
- 印尼
- 泰國
- 馬來西亞
- 新加坡
- 越南
- 其他亞太國家
南美洲
- 巴西
- 阿根廷
- 哥倫比亞
- 智利
- 秘魯
- 其他南美國家
世界其他地區（RoW）
- 中東
  - 沙烏地阿拉伯
  - 阿拉伯聯合大公國
  - 卡達
  - 以色列
  - 其他中東國家
- 非洲
  - 南非
  - 埃及
  - 摩洛哥
  - 其他非洲國家

第11章策略市場資訊

工業價值網路和供應鏈評估
空白區域和機會地圖
產品演進與市場生命週期分析
通路、經銷商和打入市場策略的評估

第12章產業趨勢與策略舉措

併購
夥伴關係、聯盟和合資企業
新產品發布和認證
擴大生產能力和投資
其他策略舉措

第13章：公司簡介

NVIDIA Corporation
Intel Corporation
Qualcomm Technologies, Inc.
Advanced Micro Devices, Inc.
Alphabet Inc.
Apple Inc.
MediaTek Inc.
Samsung Electronics Co., Ltd.
Huawei Technologies Co., Ltd.
Texas Instruments Incorporated
NXP Semiconductors NV
STMicroelectronics NV
Renesas Electronics Corporation
Ambarella, Inc.
Synaptics Incorporated
Lattice Semiconductor Corporation
CEVA, Inc.
AImotive Kft.

簡介目錄圖表

Product Code: SMRC36850

According to Stratistics MRC, the Global Edge AI Inference Chips Market is accounted for $24.6 billion in 2026 and is expected to reach $43.6 billion by 2034 growing at a CAGR of 7.4% during the forecast period. Edge AI inference chips refer to specialized semiconductor processors designed to execute artificial intelligence and machine learning inference workloads locally on edge devices without relying on cloud connectivity. These chips integrate dedicated neural processing units, hardware accelerators, and optimized memory architectures to deliver high-throughput, low-latency AI computation within tight power budgets. Available as CPUs, GPUs, ASICs, FPGAs, and system-on-chip configurations, they enable real-time computer vision, natural language processing, sensor fusion, and autonomous navigation across smartphones, surveillance systems, autonomous vehicles, drones, and industrial robots.

Market Dynamics:

Driver:

Autonomous vehicle AI acceleration

Rapid global commercialization of autonomous vehicles and advanced driver assistance systems is generating substantial demand for high-performance edge AI inference chips capable of processing multi-sensor data streams in real time with safety-critical reliability. Automotive-grade inference processors must simultaneously execute computer vision, lidar fusion, and path planning algorithms within stringent power and thermal constraints. Leading automotive manufacturers and tier-one suppliers are integrating dedicated AI inference silicon into next-generation ADAS platforms. Regulatory frameworks mandating higher vehicle automation safety standards further accelerate the adoption of purpose-built automotive edge AI inference chips.

Restraint:

Complex chip design and validation cycles

Developing edge AI inference chips optimized for specific application workloads requires extensive custom silicon design, verification, and qualification processes that involve multi-year development timelines and hundreds of millions of dollars in engineering investment. The complexity of co-optimizing compute architectures, memory hierarchies, and power management circuits for diverse edge deployment environments creates formidable barriers for new market entrants. Additionally, the rapid evolution of AI model architectures necessitates continuous chip redesign cycles that challenge vendors to maintain competitive performance across product generations without sacrificing time-to-market efficiency.

Opportunity:

IoT edge intelligence proliferation

Explosive growth in connected IoT devices requiring local AI processing capabilities without continuous cloud connectivity creates a vast commercial opportunity for edge AI inference chip vendors. Smart home devices, industrial sensors, agricultural monitoring systems, and retail analytics platforms increasingly demand embedded AI inference for real-time decision making. The transition from cloud-dependent to edge-native AI architectures drives demand for ultra-low-power inference chips capable of operating on battery or energy-harvested power. Vendors offering scalable chip families covering the full power-performance spectrum from microcontroller-class to high-performance computing segments are best positioned to capture this opportunity.

Threat:

Geopolitical semiconductor supply disruptions

Escalating geopolitical tensions and export controls targeting advanced semiconductor technologies create significant supply chain risks for edge AI inference chip manufacturers dependent on specialized fabrication nodes and equipment concentrated in a limited number of geographic locations. Export restrictions on advanced chip manufacturing equipment and sub-7nm fabrication services restrict product roadmap execution for vendors reliant on leading-edge process technology. Customers increasingly require supply chain diversification from vendors, adding manufacturing complexity and cost. These geopolitical dynamics introduce procurement uncertainty that may delay enterprise and government deployment programs for edge AI inference solutions.

Covid-19 Impact:

COVID-19 disrupted global semiconductor supply chains, causing significant edge AI chip shortages that delayed deployment programs across automotive, industrial, and consumer electronics sectors. However, the pandemic simultaneously accelerated digital transformation and remote monitoring requirements that increased long-term demand for edge AI inference capabilities. Post-pandemic investments in supply chain resilience and domestic semiconductor manufacturing capacity have strengthened the structural foundations for sustained edge AI chip market growth throughout the forecast period.

The SoC-based inference accelerators segment is expected to be the largest during the forecast period

The SoC-based inference accelerators segment is expected to account for the largest market share during the forecast period, due to their highly integrated design combining CPU, GPU, neural processing units, memory controllers, and peripheral interfaces within a single chip package that delivers optimal performance-per-watt efficiency for mainstream edge AI applications. Consumer electronics, smart cameras, and IoT gateway applications favor SoC-based solutions for their cost efficiency and compact form factor. Continuous advances in multi-core SoC architecture and AI-optimized instruction sets sustain the segment's commercial leadership across diverse edge deployment contexts.

The above 28 nm segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the above 28 nm segment is predicted to witness the highest growth rate, driven by strong demand for cost-effective edge AI inference chips in price-sensitive IoT, industrial sensor, and embedded computing applications that do not require leading-edge process nodes. Mature process nodes above 28 nm offer superior cost-per-unit economics, higher manufacturing availability, and proven long-term supply reliability for volume edge deployments. Growing adoption of AI at the extreme edge in agricultural, infrastructure monitoring, and retail applications, where ultra-low chip cost is decisive, further sustains this segment's rapid expansion.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to the presence of dominant edge AI chip designers including NVIDIA Corporation, Intel Corporation, Qualcomm Technologies, Inc., and Apple Inc., combined with the highest concentration of autonomous vehicle, defense, and industrial AI application development programs. Strong fabless semiconductor ecosystem depth and significant venture and corporate R&D investment in AI silicon innovation reinforce regional technology leadership. US government initiatives supporting domestic semiconductor manufacturing and AI infrastructure investment further strengthen North America's market position.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, due to massive consumer electronics manufacturing volumes, rapid 5G and IoT device deployment, and aggressive domestic semiconductor development programs in China, South Korea, Taiwan, and Japan. The region's enormous smartphone and smart device production base creates sustained high-volume demand for edge AI inference chips. Government semiconductor self-sufficiency strategies and substantial public investment in domestic chip design and fabrication capabilities accelerate regional production capacity expansion throughout the forecast period.

Key players in the market

Some of the key players in Edge AI Inference Chips Market include NVIDIA Corporation, Intel Corporation, Qualcomm Technologies, Inc., Advanced Micro Devices, Inc., Alphabet Inc., Apple Inc., MediaTek Inc., Samsung Electronics Co., Ltd., Huawei Technologies Co., Ltd., Texas Instruments Incorporated, NXP Semiconductors N.V., STMicroelectronics N.V., Renesas Electronics Corporation, Ambarella, Inc., Synaptics Incorporated, Lattice Semiconductor Corporation, CEVA, Inc., and AImotive Kft..

Key Developments:

In May 2026, NVIDIA Corporation launched the Jetson Thor edge AI inference module, delivering next-generation transformer model inference performance for autonomous robots and industrial AI applications with a 10x improvement in energy efficiency over the previous generation.

In April 2026, Qualcomm Technologies, Inc. introduced the Snapdragon X85 AI-enhanced chipset with an upgraded Hexagon NPU delivering 75 TOPS on-device inference performance, enabling advanced generative AI and real-time computer vision on premium smartphones and edge devices.

In February 2026, Ambarella, Inc. unveiled its CV75S edge AI vision processor targeting smart camera and autonomous vehicle applications, combining 8K video processing with integrated neural network inference acceleration optimized for advanced computer vision tasks.

Chip Types Covered:

CPU-based Inference Chips
GPU-based Inference Chips
ASIC-based Inference Chips
FPGA-based Inference Chips
SoC-based Inference Accelerators
Neuromorphic Inference Chips

Process Nodes Covered:

Above 28 nm
16 nm to 28 nm
7 nm to 16 nm
Below 7 nm

Power Consumptions Covered:

Less than 5W
5W to 15W
15W to 30W
Above 30W

Applications Covered:

Computer Vision
Natural Language Processing
Sensor Data Fusion
Predictive Maintenance
Autonomous Navigation

End Users Covered:

Smartphones and Tablets
Smart Cameras and Surveillance Systems
Autonomous Vehicles and ADAS
Drones and UAVs
Industrial Robots and Cobots
Smart Home Devices

Regions Covered:

North America
- United States
- Canada
- Mexico
Europe
- United Kingdom
- Germany
- France
- Italy
- Spain
- Netherlands
- Belgium
- Sweden
- Switzerland
- Poland
- Rest of Europe
Asia Pacific
- China
- Japan
- India
- South Korea
- Australia
- Indonesia
- Thailand
- Malaysia
- Singapore
- Vietnam
- Rest of Asia Pacific
South America
- Brazil
- Argentina
- Colombia
- Chile
- Peru
- Rest of South America
Rest of the World (RoW)
- Middle East
Saudi Arabia
United Arab Emirates
Qatar
Israel
Rest of Middle East
- Africa
South Africa
Egypt
Morocco
Rest of Africa

What our report offers:

Market share assessments for the regional and country-level segments
Strategic recommendations for the new entrants
Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
Strategic recommendations in key business segments based on the market estimations
Competitive landscaping mapping the key common trends
Company profiling with detailed strategies, financials, and recent developments
Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

Company Profiling
- Comprehensive profiling of additional market players (up to 3)
- SWOT Analysis of key players (up to 3)
Regional Segmentation
- Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
Competitive Benchmarking
- Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

1 Executive Summary

1.1 Market Snapshot and Key Highlights
1.2 Growth Drivers, Challenges, and Opportunities
1.3 Competitive Landscape Overview
1.4 Strategic Insights and Recommendations

2 Research Framework

2.1 Study Objectives and Scope
2.2 Stakeholder Analysis
2.3 Research Assumptions and Limitations
2.4 Research Methodology
- 2.4.1 Data Collection (Primary and Secondary)
- 2.4.2 Data Modeling and Estimation Techniques
- 2.4.3 Data Validation and Triangulation
- 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

3.1 Market Definition and Structure
3.2 Key Market Drivers
3.3 Market Restraints and Challenges
3.4 Growth Opportunities and Investment Hotspots
3.5 Industry Threats and Risk Assessment
3.6 Technology and Innovation Landscape
3.7 Emerging and High-Growth Markets
3.8 Regulatory and Policy Environment
3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

4.1 Porter's Five Forces Analysis
- 4.1.1 Supplier Bargaining Power
- 4.1.2 Buyer Bargaining Power
- 4.1.3 Threat of Substitutes
- 4.1.4 Threat of New Entrants
- 4.1.5 Competitive Rivalry
4.2 Market Share Analysis of Key Players
4.3 Product Benchmarking and Performance Comparison

5 Global Edge AI Inference Chips Market, By Chip Type

5.1 CPU-based Inference Chips
5.2 GPU-based Inference Chips
5.3 ASIC-based Inference Chips
5.4 FPGA-based Inference Chips
5.5 SoC-based Inference Accelerators
5.6 Neuromorphic Inference Chips

6 Global Edge AI Inference Chips Market, By Process Node

6.1 Above 28 nm
6.2 16 nm to 28 nm
6.3 7 nm to 16 nm
6.4 Below 7 nm

7 Global Edge AI Inference Chips Market, By Power Consumption

7.1 Less than 5W
7.2 5W to 15W
7.3 15W to 30W
7.4 Above 30W

8 Global Edge AI Inference Chips Market, By Application

8.1 Computer Vision
- 8.1.1 Object Detection and Recognition
- 8.1.2 Facial Recognition
- 8.1.3 Video Analytics
8.2 Natural Language Processing
8.3 Sensor Data Fusion
8.4 Predictive Maintenance
8.5 Autonomous Navigation

9 Global Edge AI Inference Chips Market, By End User

9.1 Smartphones and Tablets
9.2 Smart Cameras and Surveillance Systems
9.3 Autonomous Vehicles and ADAS
9.4 Drones and UAVs
9.5 Industrial Robots and Cobots
9.6 Smart Home Devices

10 Global Edge AI Inference Chips Market, By Geography

10.1 North America
- 10.1.1 United States
- 10.1.2 Canada
- 10.1.3 Mexico
10.2 Europe
- 10.2.1 United Kingdom
- 10.2.2 Germany
- 10.2.3 France
- 10.2.4 Italy
- 10.2.5 Spain
- 10.2.6 Netherlands
- 10.2.7 Belgium
- 10.2.8 Sweden
- 10.2.9 Switzerland
- 10.2.10 Poland
- 10.2.11 Rest of Europe
10.3 Asia Pacific
- 10.3.1 China
- 10.3.2 Japan
- 10.3.3 India
- 10.3.4 South Korea
- 10.3.5 Australia
- 10.3.6 Indonesia
- 10.3.7 Thailand
- 10.3.8 Malaysia
- 10.3.9 Singapore
- 10.3.10 Vietnam
- 10.3.11 Rest of Asia Pacific
10.4 South America
- 10.4.1 Brazil
- 10.4.2 Argentina
- 10.4.3 Colombia
- 10.4.4 Chile
- 10.4.5 Peru
- 10.4.6 Rest of South America
10.5 Rest of the World (RoW)
- 10.5.1 Middle East
  - 10.5.1.1 Saudi Arabia
  - 10.5.1.2 United Arab Emirates
  - 10.5.1.3 Qatar
  - 10.5.1.4 Israel
  - 10.5.1.5 Rest of Middle East
- 10.5.2 Africa
  - 10.5.2.1 South Africa
  - 10.5.2.2 Egypt
  - 10.5.2.3 Morocco
  - 10.5.2.4 Rest of Africa

11 Strategic Market Intelligence

11.1 Industry Value Network and Supply Chain Assessment
11.2 White-Space and Opportunity Mapping
11.3 Product Evolution and Market Life Cycle Analysis
11.4 Channel, Distributor, and Go-to-Market Assessment

12 Industry Developments and Strategic Initiatives

12.1 Mergers and Acquisitions
12.2 Partnerships, Alliances, and Joint Ventures
12.3 New Product Launches and Certifications
12.4 Capacity Expansion and Investments
12.5 Other Strategic Initiatives

13 Company Profiles

13.1 NVIDIA Corporation
13.2 Intel Corporation
13.3 Qualcomm Technologies, Inc.
13.4 Advanced Micro Devices, Inc.
13.5 Alphabet Inc.
13.6 Apple Inc.
13.7 MediaTek Inc.
13.8 Samsung Electronics Co., Ltd.
13.9 Huawei Technologies Co., Ltd.
13.10 Texas Instruments Incorporated
13.11 NXP Semiconductors N.V.
13.12 STMicroelectronics N.V.
13.13 Renesas Electronics Corporation
13.14 Ambarella, Inc.
13.15 Synaptics Incorporated
13.16 Lattice Semiconductor Corporation
13.17 CEVA, Inc.
13.18 AImotive Kft.

簡介目錄圖表

List of Tables

Table 1 Global Edge AI Inference Chips Market Outlook, By Region (2023-2034) ($MN)
Table 2 Global Edge AI Inference Chips Market Outlook, By Chip Type (2023-2034) ($MN)
Table 3 Global Edge AI Inference Chips Market Outlook, By CPU-based Inference Chips (2023-2034) ($MN)
Table 4 Global Edge AI Inference Chips Market Outlook, By GPU-based Inference Chips (2023-2034) ($MN)
Table 5 Global Edge AI Inference Chips Market Outlook, By ASIC-based Inference Chips (2023-2034) ($MN)
Table 6 Global Edge AI Inference Chips Market Outlook, By FPGA-based Inference Chips (2023-2034) ($MN)
Table 7 Global Edge AI Inference Chips Market Outlook, By SoC-based Inference Accelerators (2023-2034) ($MN)
Table 8 Global Edge AI Inference Chips Market Outlook, By Neuromorphic Inference Chips (2023-2034) ($MN)
Table 9 Global Edge AI Inference Chips Market Outlook, By Process Node (2023-2034) ($MN)
Table 10 Global Edge AI Inference Chips Market Outlook, By Above 28 nm (2023-2034) ($MN)
Table 11 Global Edge AI Inference Chips Market Outlook, By 16 nm to 28 nm (2023-2034) ($MN)
Table 12 Global Edge AI Inference Chips Market Outlook, By 7 nm to 16 nm (2023-2034) ($MN)
Table 13 Global Edge AI Inference Chips Market Outlook, By Below 7 nm (2023-2034) ($MN)
Table 14 Global Edge AI Inference Chips Market Outlook, By Power Consumption (2023-2034) ($MN)
Table 15 Global Edge AI Inference Chips Market Outlook, By Less than 5W (2023-2034) ($MN)
Table 16 Global Edge AI Inference Chips Market Outlook, By 5W to 15W (2023-2034) ($MN)
Table 17 Global Edge AI Inference Chips Market Outlook, By 15W to 30W (2023-2034) ($MN)
Table 18 Global Edge AI Inference Chips Market Outlook, By Above 30W (2023-2034) ($MN)
Table 19 Global Edge AI Inference Chips Market Outlook, By Application (2023-2034) ($MN)
Table 20 Global Edge AI Inference Chips Market Outlook, By Computer Vision (2023-2034) ($MN)
Table 21 Global Edge AI Inference Chips Market Outlook, By Object Detection and Recognition (2023-2034) ($MN)
Table 22 Global Edge AI Inference Chips Market Outlook, By Facial Recognition (2023-2034) ($MN)
Table 23 Global Edge AI Inference Chips Market Outlook, By Video Analytics (2023-2034) ($MN)
Table 24 Global Edge AI Inference Chips Market Outlook, By Natural Language Processing (2023-2034) ($MN)
Table 25 Global Edge AI Inference Chips Market Outlook, By Sensor Data Fusion (2023-2034) ($MN)
Table 26 Global Edge AI Inference Chips Market Outlook, By Predictive Maintenance (2023-2034) ($MN)
Table 27 Global Edge AI Inference Chips Market Outlook, By Autonomous Navigation (2023-2034) ($MN)
Table 28 Global Edge AI Inference Chips Market Outlook, By End User (2023-2034) ($MN)
Table 29 Global Edge AI Inference Chips Market Outlook, By Smartphones and Tablets (2023-2034) ($MN)
Table 30 Global Edge AI Inference Chips Market Outlook, By Smart Cameras and Surveillance Systems (2023-2034) ($MN)
Table 31 Global Edge AI Inference Chips Market Outlook, By Autonomous Vehicles and ADAS (2023-2034) ($MN)
Table 32 Global Edge AI Inference Chips Market Outlook, By Drones and UAVs (2023-2034) ($MN)
Table 33 Global Edge AI Inference Chips Market Outlook, By Industrial Robots and Cobots (2023-2034) ($MN)
Table 34 Global Edge AI Inference Chips Market Outlook, By Smart Home Devices (2023-2034) ($MN)